import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     				=		logging.get_logger(__name__)






def 					snake_case    (			snake_case__     :Any  ,      snake_case__     :Optional[Any]  ,      snake_case__     :str)		->  Tuple:
       _A	  =    UniSpeechSatForSequenceClassification.from_pretrained(a__  ,      config=a__)
       _A	  =    downstream_dict["""projector.weight"""]
       _A	  =    downstream_dict["""projector.bias"""]
       _A	  =    downstream_dict["""model.post_net.linear.weight"""]
       _A	  =    downstream_dict["""model.post_net.linear.bias"""]
       return model






def 					snake_case    (			snake_case__     :Tuple  ,      snake_case__     :Union[str, Any]  ,      snake_case__     :List[Any])		->  Union[str, Any]:
       _A	  =    UniSpeechSatForAudioFrameClassification.from_pretrained(a__  ,      config=a__)
       _A	  =    downstream_dict["""model.linear.weight"""]
       _A	  =    downstream_dict["""model.linear.bias"""]
       return model






def 					snake_case    (			snake_case__     :int  ,      snake_case__     :int  ,      snake_case__     :Union[str, Any])		->  List[str]:
       _A	  =    UniSpeechSatForXVector.from_pretrained(a__  ,      config=a__)
       _A	  =    downstream_dict["""connector.weight"""]
       _A	  =    downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel):
              _A	  =    downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              _A	  =    downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       _A	  =    downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       _A	  =    downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       _A	  =    downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       _A	  =    downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       _A	  =    downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def 					snake_case    (			snake_case__     :int  ,      snake_case__     :List[Any]  ,      snake_case__     :str  ,      snake_case__     :Union[str, Any])		->  Union[str, Any]:
       _A	  =    torch.load(a__  ,      map_location="""cpu""")

       _A	  =    checkpoint["""Downstream"""]

       _A	  =    UniSpeechSatConfig.from_pretrained(a__)
       _A	  =    WavaVecaFeatureExtractor.from_pretrained(
           a__  ,      return_attention_mask=a__  ,      do_normalize=a__)

       _A	  =    hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""):
              _A	  =    convert_classification(a__  ,      a__  ,      a__)
       elif arch.endswith("""ForAudioFrameClassification"""):
              _A	  =    convert_diarization(a__  ,      a__  ,      a__)
       elif arch.endswith("""ForXVector"""):
              _A	  =    convert_xvector(a__  ,      a__  ,      a__)
       else:
              raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''')

       if hf_config.use_weighted_layer_sum:
              _A	  =    checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(a__)
       hf_model.save_pretrained(a__)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE     				=		argparse.ArgumentParser()
    parser.add_argument(
        '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
    )
    parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
    parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
    _SCREAMING_SNAKE_CASE     				=		parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




